LID SPN TRAP

1429171_a_at

Ncapg non-SMC condensin I complex, subunit G

AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.724 0.405 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.723
Model: OLS Adj. R-squared: 0.680
Method: Least Squares F-statistic: 16.55
Date: Tue, 04 Nov 2025 Prob (F-statistic): 1.57e-05
Time: 06:17:49 Log-Likelihood: -98.330
No. Observations: 23 AIC: 204.7
Df Residuals: 19 BIC: 209.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -33.2065 87.472 -0.380 0.708 -216.287 149.874
C(dose)[T.1] 280.3230 111.547 2.513 0.021 46.853 513.793
expression 18.0122 17.988 1.001 0.329 -19.637 55.661
expression:C(dose)[T.1] -48.2220 23.377 -2.063 0.053 -97.150 0.706
Omnibus: 1.557 Durbin-Watson: 1.708
Prob(Omnibus): 0.459 Jarque-Bera (JB): 1.226
Skew: 0.365 Prob(JB): 0.542
Kurtosis: 2.135 Cond. No. 187.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 19.53
Date: Tue, 04 Nov 2025 Prob (F-statistic): 1.99e-05
Time: 06:17:49 Log-Likelihood: -100.65
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 105.3583 60.415 1.744 0.097 -20.666 231.382
C(dose)[T.1] 50.8812 9.086 5.600 0.000 31.928 69.834
expression -10.5397 12.388 -0.851 0.405 -36.381 15.302
Omnibus: 2.151 Durbin-Watson: 1.858
Prob(Omnibus): 0.341 Jarque-Bera (JB): 1.175
Skew: 0.174 Prob(JB): 0.556
Kurtosis: 1.949 Cond. No. 70.1

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Tue, 04 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 06:17:49 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.130
Model: OLS Adj. R-squared: 0.089
Method: Least Squares F-statistic: 3.146
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0906
Time: 06:17:49 Log-Likelihood: -111.50
No. Observations: 23 AIC: 227.0
Df Residuals: 21 BIC: 229.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 234.1984 87.362 2.681 0.014 52.520 415.877
expression -32.5797 18.370 -1.774 0.091 -70.781 5.622
Omnibus: 1.277 Durbin-Watson: 2.324
Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.867
Skew: -0.005 Prob(JB): 0.648
Kurtosis: 2.049 Cond. No. 64.4

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
1.136 0.308 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.527
Model: OLS Adj. R-squared: 0.398
Method: Least Squares F-statistic: 4.082
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0356
Time: 06:17:49 Log-Likelihood: -69.689
No. Observations: 15 AIC: 147.4
Df Residuals: 11 BIC: 150.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 102.2711 143.249 0.714 0.490 -213.019 417.561
C(dose)[T.1] 226.1313 214.956 1.052 0.315 -246.983 699.246
expression -6.6158 27.118 -0.244 0.812 -66.301 53.070
expression:C(dose)[T.1] -34.7259 41.353 -0.840 0.419 -125.743 56.292
Omnibus: 0.106 Durbin-Watson: 1.239
Prob(Omnibus): 0.948 Jarque-Bera (JB): 0.057
Skew: -0.012 Prob(JB): 0.972
Kurtosis: 2.698 Cond. No. 197.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.413
Method: Least Squares F-statistic: 5.915
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0163
Time: 06:17:50 Log-Likelihood: -70.155
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 180.9160 107.055 1.690 0.117 -52.336 414.168
C(dose)[T.1] 46.0946 15.323 3.008 0.011 12.709 79.480
expression -21.5487 20.220 -1.066 0.308 -65.604 22.507
Omnibus: 0.580 Durbin-Watson: 1.119
Prob(Omnibus): 0.748 Jarque-Bera (JB): 0.590
Skew: -0.371 Prob(JB): 0.745
Kurtosis: 2.373 Cond. No. 77.3

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.00629
Time: 06:17:50 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.117
Model: OLS Adj. R-squared: 0.049
Method: Least Squares F-statistic: 1.717
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.213
Time: 06:17:50 Log-Likelihood: -74.370
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 265.4627 131.446 2.020 0.065 -18.509 549.435
expression -33.1028 25.261 -1.310 0.213 -87.676 21.470
Omnibus: 0.184 Durbin-Watson: 1.540
Prob(Omnibus): 0.912 Jarque-Bera (JB): 0.305
Skew: 0.211 Prob(JB): 0.859
Kurtosis: 2.444 Cond. No. 74.3